The challenge in detecting behind the wall stationary targets appears when the target is close to the wall and has a
frequency response that is fully or partially overlapping with the wall's spectrum. In order to detect such targets,
background subtraction is usually applied. The main challenge of using this method is the availability of the empty
scene, which is typically unavailable to the user. In this paper, we introduce an adaptive background estimation and
subtraction technique, to detect objects behind the wall with the focus on human detection. This technique is based on
the architecture of the adaptive side-lobe canceller, where a number of antenna elements are used to form a subarray that
captures the background in the main beam, while receiving the incident scatterings from the target in the sidelobes. The
output of this subarray is then used as the reference signal to suppress the background components at the output of each
sensor, through adaptive Recursive Least Squares (RLS) algorithm. This technique can be used with both co- and cross-polarization
returns, in order to further reduce the effect of the background and enhance the detectability of the target.
Target detection and classification are considered the primary tasks in through-the-wall radar imaging. Indoor targets can
be stationary or in motion. In this paper, we apply the matched illumination concept to the scattered electromagnetic
field of two stationary targets that are commonly found in an indoor environment, namely, a wooden chair and a wooden
table. The optimal waveform was obtained by choosing the eigenvector corresponding to the largest eigenvalue of the
target's autocorrelation matrix. The scattered field over the frequency band of 1-3 GHz was obtained by full wave
numerical simulations using a commercially available
Finite-Difference Time Domain solver (XFDTD from
REMCOM). The detection performance of the optimum waveform against the commonly used linear frequency
modulated (LFM) signal of the same bandwidth was compared.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.